Deep Convolution Neural Networks (Deep CNNs) are at the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve the problems of character recognition, but their use has become as widespread as it is now thanks to recent research. These deep CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolutional neural network became a very useful tool in the field of the machine learning.
The CNN is comprised of a feature extractor which extracts features from an image, and a feature classifier which detects objects in the image or recognizes the image by referring to the features extracted by the feature extractor.
Further, the feature extractor of the CNN is comprised of convolutional layers, and the feature classifier is comprised of FC layers capable of applying fully connected operations to the features extracted by the feature extractor.
However, the FC layers have problems in that weights have to be generated for every feature inputted, and the fully connected operations have to be performed for every feature inputted, resulting in heavy computational load.
Further, the FC layers require a size of its input image to be same as a size preset according to an FC layer model. Therefore, if a training image or a test image having a size different from the preset size is inputted into a CNN including the FC layers, although the convolutional layers may apply their operations successfully to the training image or the test image, the FC layers cannot apply their operations to the training image or the test image, as the size of the input image is different from the preset size.
Accordingly, the inventors of the present disclosure propose the CNN for image recognition capable of overcoming the problems of the FC layers.